this post was submitted on 15 Aug 2024
617 points (97.1% liked)
memes
10247 readers
2972 users here now
Community rules
1. Be civil
No trolling, bigotry or other insulting / annoying behaviour
2. No politics
This is non-politics community. For political memes please go to [email protected]
3. No recent reposts
Check for reposts when posting a meme, you can only repost after 1 month
4. No bots
No bots without the express approval of the mods or the admins
5. No Spam/Ads
No advertisements or spam. This is an instance rule and the only way to live.
Sister communities
- [email protected] : Star Trek memes, chat and shitposts
- [email protected] : Lemmy Shitposts, anything and everything goes.
- [email protected] : Linux themed memes
- [email protected] : for those who love comic stories.
founded 1 year ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
I'm not saying normalization is a bad strategy, just that it, like any other processing technique comes with limitations and requires extra attention to avoid incorrect conclusions when interpreting the results.
If you were to attempt to trap and tag bigfoots in both areas, would you end up with 100 times as many angry people in a gorilla suit in the small town? No. You would end up with 1 in both areas. So while the tiny town does technically have 100x the density per capita, each region has only one observable suit wearer.
Assuming the distribution of gorilla suit wearers is uniform, you would expect approximately 99 tiny towns with no big foot sightings for every 1 town with a sighting. So if you were to sample random small towns, because the map says big foots live near small towns, you would actually see fewer hairy beasts than your peer who decided to sample areas with higher population density.
If we could have fractional observations, then all this would be a lot more straightforward, but the discrete nature of the subject matter makes the data imherently noisy. Interpreting data involving discrete events is a whole art and usually involves a lot of filtering.